Showing papers on "Growing season published in 2021"

Water uptake depth is coordinated with leaf water potential, water-use efficiency and drought vulnerability in karst vegetation.

Abstract: Root access to bedrock water storage or groundwater is an important trait allowing plant survival in seasonally dry environments. However, the degree of coordination between water uptake depth, leaf-level water-use efficiency (WUEi) and water potential in drought-prone plant communities is not well understood. We conducted a 135-d rainfall exclusion experiment in a subtropical karst ecosystem with thin skeletal soils to evaluate the responses of 11 co-occurring woody species of contrasting life forms and leaf habits to a severe drought during the wet growing season. Marked differences in xylem water isotopic composition during drought revealed distinct ecohydrological niche separation among species. The contrasting behaviour of leaf water potential in coexisting species during drought was largely explained by differences in root access to deeper, temporally stable water sources. Smaller-diameter species with shallower water uptake, more negative water potentials and lower WUEi showed extensive drought-induced canopy defoliation and/or mortality. By contrast, larger-diameter species with deeper water uptake, higher leaf-level WUEi and more isohydric behaviour survived drought with only moderate canopy defoliation. Severe water limitation imposes strong environmental filtering and/or selective pressures resulting in tight coordination between tree diameter, water uptake depth, iso/anisohydric behaviour, WUEi and drought vulnerability in karst plant communities.

Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities

Abstract: Crop phenology is critical for agricultural management, crop yield estimation, and agroecosystem assessment. Traditionally, crop growth stages are observed from the ground, which is time-consuming and lacks spatial variability. Remote sensing Vegetation Index (VI) time series has been used to map land surface phenology (LSP) and relate to crop growth stages mostly after the growing season. In recent years, high temporal and spatial resolution remote sensing data have allowed near-real-time mapping of crop phenology within the growing season. This paper summarizes two classes of near-real-time mapping methods, i.e., curve-based and trend-based approaches. The curve-based approaches combine the time series VIs and crop growth stages from historical years with the current observations to estimate crop growth stages. The curve-based approaches are capable of a short-term prediction. The trend-based approaches detect upward or downward trends from time series and confirm the trends using the increasing or decreasing momentum and VI thresholds. The trend-based approaches only use current observations. Both curve-based and trend-based approaches are promising in mapping crop growth stages timely. Nevertheless, mapping crop phenology near real-time is challenging since remote sensing observations are not always sensitive to crop growth stages. The accuracy of crop phenology detection depends on the frequency and availability of cloud-free observations within the growing season. Recent satellite datasets such as the harmonized Landsat and Sentinel-2 (HLS) are promising for mapping crop phenology within the season over large areas. Operational applications in the near future are feasible.

An increase in xylem embolism resistance of grapevine leaves during the growing season is coordinated with stomatal regulation, turgor loss point and intervessel pit membranes.

Abstract: Although xylem embolism resistance is traditionally considered as static, we hypothesized that in grapevine (Vitis vinifera) leaf xylem becomes more embolism-resistant over the growing season. We evaluated xylem architecture, turgor loss point (ΨTLP ) and water potentials leading to 25% of maximal stomatal conductance (gs25 ) or 50% embolism in the leaf xylem (P50 ) in three irrigation treatments and at three time points during the growing season, while separating the effects of leaf age and time of season. Hydraulic traits acclimated over the growing season in a coordinated manner. Without irrigation, ΨTLP , gs25 , and P50 decreased between late May and late August by 0.95, 0.77 and 0.71 MPa, respectively. A seasonal shift in P50 occurred even in mature leaves, while irrigation had only a mild effect (< 0.2 MPa) on P50 . Vessel size and pit membrane thickness were also seasonally dynamic, providing a plausible explanation for the shift in P50 . Our findings provide clear evidence that grapevines can modify their hydraulic traits along a growing season to allow lower xylem water potential, without compromising gas exchange, leaf turgor or xylem integrity. Seasonal changes should be considered when modeling ecosystem vulnerability to drought or comparing datasets acquired at different phenological stages.

Experimental warming differentially affects vegetative and reproductive phenology of tundra plants

Abstract: Rapid climate warming is altering Arctic and alpine tundra ecosystem structure and function, including shifts in plant phenology. While the advancement of green up and flowering are well-documented, it remains unclear whether all phenophases, particularly those later in the season, will shift in unison or respond divergently to warming. Here, we present the largest synthesis to our knowledge of experimental warming effects on tundra plant phenology from the International Tundra Experiment. We examine the effect of warming on a suite of season-wide plant phenophases. Results challenge the expectation that all phenophases will advance in unison to warming. Instead, we find that experimental warming caused: (1) larger phenological shifts in reproductive versus vegetative phenophases and (2) advanced reproductive phenophases and green up but delayed leaf senescence which translated to a lengthening of the growing season by approximately 3%. Patterns were consistent across sites, plant species and over time. The advancement of reproductive seasons and lengthening of growing seasons may have significant consequences for trophic interactions and ecosystem function across the tundra.

Evidence of climate-induced stress of Norway spruce along elevation gradient preceding the current dieback in Central Europe

Abstract: Effect of drought during 2017 and 2018 resulted in radial stem increment reduction to 78% and 61%, respectively, of the levels occurring in normal year 2016 in Central Europe. Norway spruce (Picea abies (L.) Karst.) is currently the most threatened commercial tree species in Central Europe. This is due to increased drought stress from advancing climate change as well as the species’ distribution outside its natural range. Tree water status and water movement through a tree are key parameters influencing tree growth and vitality. This study is focused on the growth and stress reaction of spruce to climatic conditions, analysing stem diameter variation along an elevation gradient (381–995 m a.s.l.) in the Czech Republic. Tree water deficit based on the zero-growth concept (TWD), calculated from high-frequency dendrometer records and the temporal dynamics of radial growth, was studied for 3 years (2016–2018). Two of these 3 years were affected by severe drought during the growing season. Contrary to our expectations, the observed TWD showed no clear linear decline with rising elevation. The most severe tree desiccation was observed in experimental sites at middle elevations of about 600 m a.s.l. Here, we show that both the timing and level of tree water deficit had an impact on annual stem radial increment (SRIannual). Severe drought had a substantial negative impact on SRIannual of Norway spruce in both 2017 and 2018. Drought conditions in 2017 and 2018 resulted in reduction of SRIannual relative to measurements for the wetter year in 2016 to 78% and 61%, respectively. We report the evidence that the current climatic conditions in the Central European region are not suitable for growing Norway spruce at lower and middle elevations and that forest management needs to react immediately to this situation.

Satellite-based data fusion crop type classification and mapping in Rio Grande do Sul, Brazil

Abstract: Field-scale crop monitoring is essential for agricultural management and policy making for food security and sustainability. Automating crop classification process while elaborating a workflow is a key step for reliable and precise crop mapping. This study aims to develop an approach for crop classification in the state of Rio Grande do Sul, Brazil, following the specific goals of i) evaluating spatial satellite-based features to guide crop data collection; ii) testing transfer learning model with subsequent growing season data; iii) examining accuracy in early-season prediction model; and lastly, iv) developing a crop classification model for estimating large scale crop area. As main data inputs, Sentinel-2, Sentinel-1, and Shuttle Radar Topographic Mission (SRTM) Digital Elevation data were used to extract features to input in the Random Forest classifier. Spatial variability of satellite features was evaluated using Moran’s I Index and cluster k-means. Crop area prediction data were obtained at municipality level to compare with census data (standard method). A crop summer map layer was generated for three major crops: soybeans (Glycine max L.), corn (Zea mays L.), and rice (Oryza sativa L.) in the state of Rio Grande do Sul, Brazil. The crop classification model achieved an overall accuracy of 0.95. Model performance was influenced by sample size and spatial variability of the samples. The random forest model was transferred to the next growing season with 0.89 and 0.91 overall accuracy for 250 and 750 samples, respectively. However, overall accuracy increased from 0.93 to 0.95 when 50 to 250 samples of same-year data was aggregated to the model. Similar accuracy was obtained for predictions done with data until March relative to when the entire season was considered, until May. When data for more growing seasons were aggregated, the model produced more accurate early season predictions (January and February). Soybean prediction area obtained the highest performance (R2 = 0.94), relative to rice (R2 = 0.90) and corn (R2 = 0.37). The rice prediction area presented a high precision, but the crop area was overestimated due to errors with wetland target relative to other class. Lastly, this study presents the first crop map layer of the three major field crops for the state of Rio Grande do Sul, Brazil, serving as a foundation for the creation of crop type maps for other states in the country and around the globe.

Response of vegetation phenology to soil moisture dynamics in the Mongolian Plateau

Abstract: Soil moisture (SM) plays a paramount role in maintaining plant growth. However, the understanding of the influences of SM dynamics on vegetation phenology is still insufficient. This is especially true for the arid and semi-arid regions, where moisture is the main limiting factor for vegetation growth. Therefore, by selecting the typical arid and semi-arid Mongolian Plateau as the study region, we evaluated the dynamics of SM and drought characterized by the standardized soil moisture index (SSMI) from 1982 to 2019 based on SM product derived from the fifth generation of the European Center for Medium-Range Weather Forecasts Interim Re-Analysis (ERA5). Their impacts on vegetation phenological indices, including the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (LOS), were further investigated. Our results showed that SM in the growing season had experienced a significant negative trend accompanying with the increment of SM droughts. A significantly abrupt change of SM occurred around 2000. The influences of SM on vegetation phenology displayed strong seasonal variations and weakened with soil depths. Except for some alpine areas in northwestern and southeastern parts and areas with artificial plantations, negative correlations between spring SM and SOS were generally found in the Mongolian Plateau, indicating that a decreasing SM was not conductive to an earlier SOS. Most plain areas showed a positive correlation between summer SM and EOS, while the relationship became stronger in autumn. Summer SM also had a strong control on prolonging the growing season. Furthermore, vegetation phenology was sensitive to drought, with a higher sensitivity in the EOS. Our findings have derived a better understanding on the effects of climate change, but also provided vital references to maintain sustainable development of ecosystems for the Mongolian Plateau and other arid and semi-arid regions worldwide.

Winter snow and spring temperature have differential effects on vegetation phenology and productivity across Arctic plant communities

Abstract: Tundra dominates two-thirds of the unglaciated, terrestrial Arctic. Although this region has experienced rapid and widespread changes in vegetation phenology and productivity over the last several decades, the specific climatic drivers responsible for this change remain poorly understood. Here we quantified the effect of winter snowpack and early spring temperature conditions on growing season vegetation phenology (timing of the start, peak, and end of the growing season) and productivity of the dominant tundra vegetation communities of Arctic Alaska. We used daily remotely sensed normalized difference vegetation index (NDVI), and daily snowpack and temperature variables produced by SnowModel and MicroMet, coupled physically based snow and meteorological modeling tools, to (1) determine the most important snowpack and thermal controls on tundra vegetation phenology and productivity and (2) describe the direction of these relationships within each vegetation community. Our results show that soil temperature under the snowpack, snowmelt timing, and air temperature following snowmelt are the most important drivers of growing season timing and productivity among Arctic vegetation communities. Air temperature after snowmelt was the most important control on timing of season start and end, with warmer conditions contributing to earlier phenology in all vegetation communities. In contrast, the controls on the timing of peak season and productivity also included snowmelt timing and soil temperature under the snowpack, dictated in part by the snow insulating capacity. The results of this novel analysis suggest that while future warming effects on phenology may be consistent across communities of the tundra biome, warming may result in divergent, community-specific productivity responses if coupled with reduced snow insulating capacity lowers winter soil temperature and potential nutrient cycling in the soil.

Remote estimation of leaf area index (LAI) with unmanned aerial vehicle (UAV) imaging for different rice cultivars throughout the entire growing season

Abstract: Rice is one of the most important grain crops worldwide. The accurate and dynamic monitoring of Leaf Area Index (LAI) provides important information to evaluate rice growth and production. This study explores a simple method to remotely estimate LAI with Unmanned Aerial Vehicle (UAV) imaging for a variety of rice cultivars throughout the entire growing season. Forty eight different rice cultivars were planted in the study site and field campaigns were conducted once a week. For each campaign, several widely used vegetation indices (VI) were calculated from canopy reflectance obtained by 12-band UAV images, canopy height was derived from UAV RGB images and LAI was destructively measured by plant sampling. The results showed the correlation of VI and LAI in rice throughout the entire growing season was weak, and for all tested indices there existed significant hysteresis of VI vs. LAI relationship between rice pre-heading and post-heading stages. The model based on the product of VI and canopy height could reduce such hysteresis and estimate rice LAI of the whole season with estimation errors under 24%, not requiring algorithm re-parameterization for different phenology stages. The progressing phenology can affect VI vs. LAI relationship in crops, especially for rice having quite different canopy spectra and structure after its panicle exsertion. Thus the models solely using VI to estimate rice LAI are phenology-specific and have high uncertainties for post-heading stages. The model developed in this study combines both remotely sensed canopy height and VI information, considerably improving rice LAI estimation at both pre- and post-heading stages. This method can be easily and efficiently implemented in UAV platforms for various rice cultivars during the entire growing season with no rice phenology and cultivar pre-knowledge, which has great potential for assisting rice breeding and field management studies at a large scale.

Vegetation Change and Its Response to Climate Change in Yunnan Province, China

Abstract: The impact of global climate change on vegetation has become increasingly prominent over the past several decades. Understanding vegetation change and its response to climate can provide fundamental information for environmental resource management. In recent years, the arid climate and fragile ecosystem have led to great changes in vegetation in Yunnan Province, so it is very important to further study the relationship between vegetation and climate. In this study, we explored the temporal changes of normalized difference vegetation index (NDVI) in different seasons based on MOD13Q1 NDVI by the maximum value composite and then analyzed spatial distribution characteristics of vegetation using Sen’s tendency estimation, Mann–Kendall significance test, and coefficient of variation model (CV) combined with terrain factors. Finally, the concurrent and lagged effects of NDVI on climate factors in different seasons and months were discussed using the Pearson correlation coefficient. The results indicate that (1) the temporal variation of the NDVI showed that the NDVI values of different vegetation types increased at different rates, especially in growing season, spring, and autumn; (2) for spatial patterns, the NDVI, CV, and NDVI trends had strong spatial heterogeneity owning to the influence of altitudes, slopes, and aspects; and (3) the concurrent effect of vegetation on climate change indicates that the positive effect of temperature on NDVI was mainly in growing season and autumn, whereas spring NDVI was mainly influenced by precipitation. In addition, the lag effect analysis results revealed that spring precipitation has a definite inhibition effect on summer and autumn vegetation, but spring and summer temperature can promote the growth of vegetation. Meanwhile, the precipitation in the late growing season has a lag effect of 1-2 months on vegetation growth, and air temperature has a lag effect of 1 month in the middle of the growing season. Based on the above results, this study provided valuable information for ecosystem degradation and ecological environment protection in the Yunnan Province.

L-band vegetation optical depth as an indicator of plant water potential in a temperate deciduous forest stand

Abstract: . Vegetation optical depth (VOD) retrieved from microwave radiometry correlates with the total amount of water in vegetation, based on theoretical and empirical evidence. Because the total amount of water in vegetation varies with relative water content (as well as with biomass), this correlation further suggests a possible relationship between VOD and plant water potential, a quantity that drives plant hydraulic behavior. Previous studies have found evidence for that relationship on the scale of satellite pixels tens of kilometers across, but these comparisons suffer from significant scaling error. Here we used small-scale remote sensing to test the link between remotely sensed VOD and plant water potential. We placed an L-band radiometer on a tower above the canopy looking down at red oak forest stand during the 2019 growing season in central Massachusetts, United States. We measured stem xylem and leaf water potentials of trees within the stand and retrieved VOD with a single-channel algorithm based on continuous radiometer measurements and measured soil moisture. VOD exhibited a diurnal cycle similar to that of leaf and stem water potential, with a peak at approximately 05:00 eastern daylight time (UTC − 4). VOD was also positively correlated with both the measured dielectric constant and water potentials of stem xylem over the growing season. The presence of moisture on the leaves did not affect the observed relationship between VOD and stem water potential. We used our observed VOD–water-potential relationship to estimate stand-level values for a radiative transfer parameter and a plant hydraulic parameter, which compared well with the published literature. Our findings support the use of VOD for plant hydraulic studies in temperate forests.

A comparative study of irrigation techniques for energy flow and greenhouse gas (GHG) emissions in wheat agroecosystems under contrasting environments in south of Iran

Abstract: The agriculture sector in general and irrigation, in particular, can be a major emitter of greenhouse gases and can contribute to global climate change. Most studies dealing with the evaluation of irrigation planning worldwide have not taken this aspect of irrigation into account, while it is of paramount importance to attach environmental assessment of irrigation programs, in particular their impacts on greenhouse gas (GHG) emissions, to any policy-making and development efforts for planning irrigation strategies. The present study aims at describing the energy flow and GHG emissions in wheat agroecosystems under different irrigation systems in Southern Iran. Three irrigation systems, including furrow irrigation (FI), sprinkler irrigation (SI) and drip irrigation (DI) were studied in two contrasting environments with regard to pedo-climatic conditions. The results indicated that environment and irrigation system remarkably affected energy input, energy output and GHG emissions. The highest energy input was observed in DI (75.59 GJ ha−1) followed by FI (62.67 GJ ha−1) and SI systems (49.81 GJ ha−1). In DI system, the main energy consumption item belonged to polyethylene (PE) pipelines (about one-third of total input energy), while in other two irrigation systems diesel fuel consumption was the main component of energy consumption (32.77% and 36.2% in FI and SI systems, respectively). Changing the irrigation method from furrow to sprinkler and drip reduced water requirement by 43.7% and 39.6% and electricity consumption by 36.5% and 25.2%, respectively. The highest output energy (averaged over both environments) was observed in DI system (154.52 GJ ha−1), although it was not considerably different from SI system (151.41 GJ ha−1). The environment that had more suitable environmental condition and longer growing season for wheat growth showed higher output energy (167.87 GJ ha−1) compared to a less favorable environment (125.57 GJ ha−1). Pressurized irrigation systems had priority over traditional irrigation practices in terms of energy efficiencies and global warming potential (GWP) and were more environment-friendly. SI system had the highest net energy and energy use efficiency (3.02 and 0.111 kg MJ−1, respectively). Averaged over both environments, the highest direct energy, indirect energy, renewable energy and non-renewable energy consumptions were observed in FI (34.47 GJ ha−1), DI (49.26 GJ ha−1), FI (8.91 GJ ha−1) and DI (68.48 GJ ha−1) systems, respectively. Our results indicated that the total GWP on a hectare basis could be decreased by changing the irrigation system from FI (10886.1 kg CO2eq ha−1) to pressurized irrigation systems (8945.7 kg CO2eq ha−1 and 8049.7 kg CO2eq ha−1 for DI and SI system, respectively). In contrast to previous studies (which have mainly focused on methods of irrigation other than sprinkler), sprinkler irrigation was superior in these regards when compared to drip irrigation. The present study helps decision-makers to grasp a better understanding of the linkage between various methods of irrigation and the subsequent impact on GHG emissions, which is a prerequisite to any planning effort for future irrigation strategies at national and international levels.

Climate warming dominates over plant genotype in shaping the seasonal trajectory of foliar fungal communities on oak.

Abstract: Leaves interact with a wealth of microorganisms. Among these, fungi are highly diverse and are known to contribute to plant health, leaf senescence and early decomposition. However, patterns and drivers of the seasonal dynamics of foliar fungal communities are poorly understood. We used a multifactorial experiment to investigate the influence of warming and tree genotype on the foliar fungal community on the pedunculate oak Quercus robur across one growing season. Fungal species richness increased, evenness tended to decrease, and community composition strongly shifted during the growing season. Yeasts increased in relative abundance as the season progressed, while putative fungal pathogens decreased. Warming decreased species richness, reduced evenness and changed community composition, especially at the end of the growing season. Warming also negatively affected putative fungal pathogens. We only detected a minor imprint of tree genotype and warming × genotype interactions on species richness and community composition. Overall, our findings demonstrate that warming plays a larger role than plant genotype in shaping the seasonal dynamics of the foliar fungal community on oak. These warming-induced shifts in the foliar fungal community may have a pronounced impact on plant health, plant-fungal interactions and ecosystem functions.

Is a drought a drought in grasslands? Productivity responses to different types of drought.

Abstract: Drought, defined as a marked deficiency of precipitation relative to normal, occurs as periods of below-average precipitation or complete failure of precipitation inputs, and can be limited to a single season or prolonged over multiple years. Grasslands are typically quite sensitive to drought, but there can be substantial variability in the magnitude of loss of ecosystem function. We hypothesized that differences in how drought occurs may contribute to this variability. In four native Great Plains grasslands (three C4- and one C3-dominated) spanning a ~ 500-mm precipitation gradient, we imposed drought for four consecutive years by (1) reducing each rainfall event by 66% during the growing season (chronic drought) or (2) completely excluding rainfall during a shorter portion of the growing season (intense drought). The drought treatments were similar in magnitude but differed in the following characteristics: event number, event size and length of dry periods. We observed consistent drought-induced reductions (28–37%) in aboveground net primary production (ANPP) only in the C4-dominated grasslands. In general, intense drought reduced ANPP more than chronic drought, with little evidence that drought duration altered this pattern. Conversely, belowground net primary production (BNPP) was reduced by drought in all grasslands (32–64%), with BNPP reductions greater in intense vs. chronic drought treatments in the most mesic grassland. We conclude that grassland productivity responses to drought did not strongly differ between these two types of drought, but when differences existed, intense drought consistently reduced function more than chronic drought.

Plant phenology evaluation of CRESCENDO land surface models – Part 1: Start and end of the growing season

Abstract: . Plant phenology plays a fundamental role in land–atmosphere interactions, and its variability and variations are an indicator of climate and environmental changes. For this reason, current land surface models include phenology parameterizations and related biophysical and biogeochemical processes. In this work, the climatology of the beginning and end of the growing season, simulated by the land component of seven state-of-the-art European Earth system models participating in the CMIP6, is evaluated globally against satellite observations. The assessment is performed using the vegetation metric leaf area index and a recently developed approach, named four growing season types. On average, the land surface models show a 0.6-month delay in the growing season start, while they are about 0.5 months earlier in the growing season end. The difference with observation tends to be higher in the Southern Hemisphere compared to the Northern Hemisphere. High agreement between land surface models and observations is exhibited in areas dominated by broadleaf deciduous trees, while high variability is noted in regions dominated by broadleaf deciduous shrubs. Generally, the timing of the growing season end is accurately simulated in about 25 % of global land grid points versus 16 % in the timing of growing season start. The refinement of phenology parameterization can lead to better representation of vegetation-related energy, water, and carbon cycles in land surface models, but plant phenology is also affected by plant physiology and soil hydrology processes. Consequently, phenology representation and, in general, vegetation modelling is a complex task, which still needs further improvement, evaluation, and multi-model comparison.

Increasing climatic sensitivity of global grassland vegetation biomass and species diversity correlates with water availability.

Abstract: Grasslands are key repositories of biodiversity and carbon storage and are heavily impacted by effects of global warming and changes in precipitation regimes Patterns of grassland dynamics associated with variability in future climate conditions across spatiotemporal scales are yet to be adequately quantified Here, we performed a global meta-analysis of year and growing season sensitivities of vegetation aboveground biomass (AGB), aboveground net primary productivity (ANPP), and species richness (SR) and diversity (Shannon index, H) to experimental climate warming and precipitation shifts All four variables were sensitive to climate change Their sensitivities to shifts in precipitation were correlated with local background water availability, such as mean annual precipitation (MAP) and aridity, and AGB and ANPP sensitivities were greater in dry habitats than in nonwater-limited habitats There was no effect of duration of experiment (short vs long term) on sensitivities Temporal trends in ANPP and SR sensitivity depended on local water availability; ANPP sensitivity to warming increased over time and SR sensitivity to irrigation decreased over time Our results provide a global overview of the sensitivities of grassland function and diversity to climate change that will improve the understanding of ecological responses across spatiotemporal scales and inform policies for conservation in dry climates

Lagged and dormant season climate better predict plant vital rates than climate during the growing season.

Abstract: Understanding the effects of climate on the vital rates (e.g., survival, development, reproduction) and dynamics of natural populations is a long-standing quest in ecology, with ever-increasing relevance in the face of climate change. However, linking climate drivers to demographic processes requires identifying the appropriate time windows during which climate influences vital rates. Researchers often do not have access to the long-term data required to test a large number of windows, and are thus forced to make a priori choices. In this study, we first synthesize the literature to assess current a priori choices employed in studies performed on 104 plant species that link climate drivers with demographic responses. Second, we use a sliding-window approach to investigate which combination of climate drivers and temporal window have the best predictive ability for vital rates of four perennial plant species that each have over a decade of demographic data (Helianthella quinquenervis, Frasera speciosa, Cylindriopuntia imbricata, and Cryptantha flava). Our literature review shows that most studies consider time windows in only the year preceding the measurement of the vital rate(s) of interest, and focus on annual or growing season temporal scales. In contrast, our sliding-window analysis shows that in only four out of 13 vital rates the selected climate drivers have time windows that align with, or are similar to, the growing season. For many vital rates, the best window lagged more than 1 year and up to 4 years before the measurement of the vital rate. Our results demonstrate that for the vital rates of these four species, climate drivers that are lagged or outside of the growing season are the norm. Our study suggests that considering climatic predictors that fall outside of the most recent growing season will improve our understanding of how climate affects population dynamics.